Purpose: Explore data at sub-daily temporal resolutions (e.g., 15-min and 1-hour time steps) and compare outputs to daily data. Are we missing anything by aggregating and analyzing streamflow data at daily timescales? (Note limited analysis of 15-min data as Montana and Wyoming data is collected at the hourly timescale).
Visualize 15-min and 1-hour data and note diversity in timing of peak flows during events; compare to daily data.
Use event pairing to explore the mean and variation in time lags between peak flows at Big G and little g’s; compare hourly and daily data
Fit basic wedge model to the 1-hour unaligned data…do the results change so much that we need to align time series when working with sub-daily data? Compare to daily data.
Explore use of dynamic time warping to align time series data
4.1 Data
4.1.1 Load data
Bring in site info and sub-daily data
Code
# site information and locationssiteinfo <-read_csv("C:/Users/jbaldock/OneDrive - DOI/Documents/USGS/EcoDrought/EcoDrought Working/Data/EcoDrought_SiteInformation.csv")siteinfo_sp <-st_as_sf(siteinfo, coords =c("long", "lat"), crs =4326)mapview(siteinfo_sp, zcol ="designation")